This description relates to determining and reporting mobile phone distraction of a driver.
Determining and reporting mobile phone distraction of a driver is useful because, among other reasons, driver mobile phone distraction poses a significant crash and accident risk.
In an existing system developed by Cambridge Mobile Telematics serving various safe-driving mobile applications, sensor data from personal mobile devices (e.g., smartphones), in some cases augmented with sensor data from an optional device attached to a vehicle, has been used to measure the quality of driving of drivers with the goal of improving their driving to make roads safer. The sensors available on a mobile phone that are useful in achieving this goal include, but are not limited to, position sensors (e.g., the Global Positioning System, GPS), a three-axis accelerometer to measure the phone's acceleration along three orthogonal axes, and a three-axis gyroscope to measure the phone's angular velocity along the three axes.
We use the term “mobile device” to include, for example, any kind of equipment that can be carried by a user without requiring wired connection to a communication link and is subject to being used, while a user is driving, in a way that can cause distraction of the user from the driving. Mobile devices include mobile phones, for example.
In the existing system, users install a mobile application (app) on the mobile phone and drive with it. The app runs in the background, automatically detecting the start and stop of each trip using information from location and mobile phone activity APIs provided by the iOS and Android operating systems, in some cases augmented with information from the inertial sensors on the phone; or, using wireless signals from an in-vehicle device such as the tag device from Cambridge Mobile Telematics (described in patent application publication US20150312655A1 and incorporated here by reference). The mobile app then gathers sensor data from the movement sensors, including position sensors, accelerometer, and gyroscope when the user (of the phone) is driving. This sensor data is analyzed and initially processed on the mobile phone, then sent to servers in the “cloud” via a wireless network (e.g., Wi-Fi, cellular, or any other network providing connectivity to the servers). At the cloud servers, a telematics engine processes the stream of sensor data from the mobile device to accurately estimate both the dynamics of the vehicle and the movement patterns of the mobile phone within the vehicle. These computations could be run on the mobile device itself without the data being sent to cloud servers.
The estimated vehicle dynamics include map-matched positions (latitude/longitude/altitude), aspects of which are disclosed in U.S. Pat. No. 8,457,880, incorporated here by reference. The estimated vehicle dynamics also include the “longitudinal” (in the direction of the vehicle, i.e., the forward acceleration and braking) and “lateral” acceleration (e.g., cornering) of the vehicle, aspects of which are described in U.S. Pat. No. 9,228,836, incorporated here by reference. For braking, acceleration, and cornering, the system uses the accelerometer and gyroscope data from the phone—which measures the force the phone is experiencing along three orthogonal axes, typically two axes parallel to the surface (e.g., the surface of the display screen) of the phone and one axis perpendicular to the surface of the mobile phone—and transforms these signals into an estimate of the acceleration of the vehicle, while ensuring that the movement of the phone relative to the vehicle itself does not contribute. This process includes estimating and segmenting periods of time when the phone is moving in a reference frame independent of the vehicle, e.g., because the orientation of the phone was changed by the user. As discussed later, the process by which this segmentation of a trip into distinct periods when the phone was being significantly moved within the vehicle is important for the assessment of phone distraction.
The servers in the cloud also compute scores for aspects of the user's driving, taking into account factors such as patterns of hard braking, at-risk speeding, harsh acceleration, harsh cornering, amount of driving, time of driving, and the user's phone distraction. In a version called the DriveWell™ program, the servers also provide an overall score (typically over a rolling time window, such as over one day, two weeks, one month, three months, one year, etc.) and features to engage users and incentivize safer driving, such as personalized driving tips, a leaderboard where users can compare their scores (to their families, friends, neighbors, co-workers, town, state, etc.), and cash prizes for safe driving. A key aspect of the scoring is an assessment of phone distraction.
The server also applies several classifiers to the sensor data captured from the mobile device. One such classifier produces a probability or likelihood estimate as to whether the sensor data represents data from a car or some other vehicle, such as bus, train, bike, etc. Another classifier estimates, for car trips, whether the user of the device was a passenger or driver, based on both the sensor data as well as other contextual information, such as the start and end locations of the trip.
A “phone movement” or “phone distraction” classifier determines the locations and times during which a user was using her phone during a trip. Typically, the location information about distraction events are provided as a timestamped sequence of road segments, or more generally, as <start, end> latitude/longitude position tuples together with polylines connecting the start and end positions by way of zero or more intermediate positions (also called “waypoints”). In various versions of the system this feature has been termed “phone movement”, “phone motion”, “phone use”, “phone distraction”, or “distraction”.
The existing method builds on the approach disclosed in U.S. Pat. No. 9,228,836, incorporated here by reference, in which the sensor data obtained from the trip is segmented to demarcate periods during which the phone was deemed to have moved relative to the frame of reference of the vehicle (e.g., it was moved either by a user or for other reasons within a vehicle). This procedure involves the use of accelerometer and/or gyroscope data; when the dynamics of these sensors is above various thresholds, the phone is considered to have “moved”.
The movement of a phone does not by itself constitute “distraction”; it is, however, a factor indicative of distraction—a necessary but not sufficient condition. To classify whether a particular set of segments of a trip with phone movement indicates distraction, the existing method augments the inference of phone movement with two further factors: (i) was the user likely to be interacting with the device when the phone movement occurred, and (ii) was the vehicle moving at the time of the phone movement. A specific indicator of whether the user was interacting with the phone uses the phone's screen state (“on” signifies interaction) and/or the phone's phone-lock state. With respect to vehicle movement, the existing method considers a threshold vehicle speed below which the distraction is not considered to be occurring (e.g., a user may have pulled over and be looking at their phone or driving slowly).
Thus, three conditions must all hold true for a distraction to be considered risky (and therefore a distraction episode) in the existing method:                A. Phone movement as inferred from gyroscope and/or accelerometer data.        B. User interacting with phone, inferred particularly from phone-screen state and phone-lock state.        C. The vehicle speed during the segment exceeding a threshold; various statistics of the speed over the duration of the distraction can be used, including the mean, median, or maximum.        
The user is able to view on their mobile app's trip interface a scored phone distraction episode. as an overlay on a map with the road segments shown in a different color or shade from the rest of the trajectory, and including some additional information (e.g., duration of distraction, speed of vehicle). In addition, by aggregating all distraction episodes for a trip, each trip is given a phone distraction score (typically on a scale of 1 to 5 or 1 to 10). An aggregate distraction score is also computed over a rolling period of time (e.g., past two weeks of driving, or any time period). FIG. 13 illustrates this interface in the existing system.
The existing method therefore includes a concept of episodes and severity metrics, logging of periods of phone motion, in particular: duration of phone motion, speed of vehicle, and road type where the distraction occurred. The existing method relies on acceleration, gyro readings, and screen state of the mobile device, as well as acceleration from an external device to detect phone motion. Sensor readings indicating movement of the mobile device must collectively last at least a minimum duration to log a phone motion episode; and movements detected within a set time interval of each other are incorporated into the same episode. These episodes are assessed individually to assign a risk to the event, which is reported to users. The episodes are also assessed collectively in generating a distraction score to display to a user: the distraction scores are assessed per driving trip and per calendar period.